(1) Field
The disclosed methods and systems relate generally to evolutionary computing, and more particularly to interactive evolutionary computing.
(2) Description of Relevant Art
Evolutionary algorithms can generally be understood to include stochastic search methods that replicate natural biological evolution. Often, these evolutionary algorithms operate on a population of potential solutions by applying a “survival of the fittest” principle to produce approximations to a solution. A new solution set of approximations is thus created at each generation by selecting individuals according to their level of fitness in the problem domain, and breeding these selected individuals using operators emulating natural genetics. The process facilitates an evolution of populations of “individuals” that are better suited to their environment than the individuals that they were created from, just as in natural adaptation.
Evolutionary algorithms can thus model natural processes including selection, recombination, mutation, migration, locality, and neighborhood. Evolutionary algorithms are generally performed in a parallel manner, using for example, a migration, global, or diffusion model, to operate on populations of individuals rather than single solutions/individuals. Accordingly, a solution set of individuals (e.g., population) can be randomly initialized, and an objective function can be evaluated for these individuals. If optimization criteria are not met, a new generation is created where individuals are selected according to their fitness for the production of offspring. Parents can be recombined to produce offspring, and offspring can be mutated with a certain probability. The fitness of the offspring is then computed, and the offspring replace the parents in the population to provide a new generation. This cycle is performed until the optimization criteria are reached.
In some applications, evaluation of the fitness and/or objective function(s) may be subjectively influenced by an expert (or experts) in the given problem domain(s) under a given number of constraints. The fitness can be represented mathematically, visually or through another form of evaluation. The fitness and/or objective function(s) can be defined such that evolved designs perform certain tasks subject to given constraints.
Many real-world optimization problems are assignment problems whereby resources are assigned a set of tasks to perform. In such problems, the objective function, or the quantity or vector one seeks to maximize or minimize, can be productivity, speed, robustness, etc. The optimization can be further subjected to hard or soft constraints. If the resources are human beings, there are un-verbalized and/or un-formulated and/or subjective criteria and constraints such as implicit knowledge and preferences specific to each of those human beings that are not considered by the objective function. For example, in a factory scheduling application where workers are assigned jobs to reduce production time under such constraints as changeover times or compatibility, some workers may have special knowledge about the factory and/or may prefer to work on certain machines they like or know better. Such factors are not considered in the objective function, but may be crucial to worker efficiency, worker satisfaction, and overall productivity.
In another example, mailmen are assigned tours. Management's objective function is to reduce the amount of time to deliver mail to increase speed of service. Constraints may include load balancing (e.g., mailmen should have approximately the same distance and the same load to carry) and/or maximum weight a mailman can carry; however, certain mailmen may subjectively prefer certain tours because they know the terrain well and/or because the tours end close to where they live, etc. There is also implicit knowledge a mailman has about certain tours, e.g., when building caretakers are present. Generally, existing methods and systems fail to consider un-verbalized and/or un-formulated and/or subjective criteria and constraints specific to human participants in the system.